CN112950388A - Data processing method, system, device and readable storage medium - Google Patents
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Abstract
The embodiment of the invention provides a data processing method, which comprises the steps of carrying out penetrating analysis on a plurality of analysis factors through a plurality of preset penetrating decomposition formulas, transaction penetrating rules and a plurality of analysis factors to obtain a plurality of intermediate data, constructing and constructing a fund virtual transaction as a first adjustment item, constructing a cash virtual transaction as a second adjustment item, generating penetrating data of investment portfolio data according to the plurality of intermediate data, the first adjustment item and the second adjustment item, and carrying out visualization processing, wherein the penetrating data is used for representing income attribution investment portfolio data of the investment portfolio data. The invention analyzes the investment portfolio data by a plurality of analysis factors such as share ratio data and the like, a plurality of penetration decomposition type, virtual transaction, cash flow data and the like, thereby ensuring the accuracy of data processing, simplifying the calculated amount of the data, improving the processing efficiency of the data and lightening the operation burden of a CPU.
Description
Technical Field
The embodiment of the invention relates to the technical field of big data, in particular to a data processing method, a data processing system, computer equipment and a computer readable storage medium.
Background
The attribution of the revenue is an important part of the portfolio investment. In the existing profit systems, some profit systems acquire and push profit data according to current position data, and the profit systems easily ignore the influence of trading behaviors on the profit data of combined investment. Some revenue systems ignore the influence of penetration, and the penetrated data cannot be well obtained through the revenue system, so that the penetration revenue cannot be analyzed. Although some systems try to perform penetration position taking and trading, the processing range of data is often limited, only 100% held funds can be subjected to penetration analysis, which brings great limitation to the system for revenue capacity analysis and easily causes inaccurate revenue analysis results.
In the revenue system, a large amount of complex data calculation is required in the process of processing the combined investment data, the calculation amount is large, and the calculation burden of the CPU is easily caused.
Disclosure of Invention
In view of this, embodiments of the present invention provide a data processing method, a system, a computer device, and a computer readable storage medium, which are used to solve the problem that the existing revenue system processing portfolio investment data needs to perform a large amount of complex data calculation, and the calculation load of a CPU is easily caused by a large calculation amount.
The embodiment of the invention solves the technical problems through the following technical scheme:
a method of data processing, comprising:
extracting a plurality of analysis factors of the investment portfolio data, classifying and packaging the analysis factors in a preset database, wherein the analysis factors comprise combined position data, combined transaction data and share ratio data;
acquiring the combined position data and share ratio data from the database, and performing a penetrating decomposition operation on the combined position data and the share ratio data according to a plurality of preset penetrating decomposition formulas to obtain a plurality of intermediate data, wherein the plurality of intermediate data comprise end position data, initial position data and transaction position data of a plurality of tickets in the investment combination data;
constructing a fund virtual transaction according to a preset fund virtual transaction template, wherein the fund virtual transaction is obtained by updating the fund virtual transaction template through the share ratio data and is used for simulating the subscription and redemption of the investment portfolio data;
acquiring the combined transaction data and share ratio data from the database, and performing a penetrating decomposition operation on the combined transaction data and share ratio data through a plurality of preset penetrating decomposition formulas to obtain a plurality of mother fund market value data, transaction data of each bottom layer data and market value data of each bottom layer data in investment combination data, wherein each mother fund comprises at least one bottom layer data;
according to the market value data of each mother fund and the corresponding market value data of the bottom layer data, cash virtual transactions are constructed and used as second adjustment items, and the cash virtual transactions are used for expressing the occupation ratio of cash in corresponding sub-funds in the investment portfolio data; and
and generating penetration data of the investment portfolio data according to the plurality of intermediate data, the first adjustment items and the second adjustment items, and performing visualization processing, wherein the penetration data is used for representing income attribution of the investment portfolio data.
Optionally, the preset multiple penetration decompositions include a first penetration decompositions, and the step of performing the penetration decomposition operation on the combined position taken data and the share ratio data according to the preset multiple penetration decompositions to obtain multiple intermediate data includes:
acquiring share ratio data and end market value data of each coupon of the portfolio data at the final time of a target time period;
and calculating to obtain the end position data corresponding to each ticket of the investment portfolio data according to the first breakthrough decomposition formula and the share ratio data and the end market value data of each ticket of the investment portfolio data in the final time of the target time period.
Optionally, the preset multiple penetration decompositions include a second penetration decompositions, and the step of performing the penetration decomposition operation on the combined position taken data and the share ratio data according to the preset multiple penetration decompositions to obtain multiple intermediate data includes:
acquiring share ratio data and initial market value data of each coupon of the investment portfolio data at the initial time of a target time period;
and calculating to obtain initial position data corresponding to each ticket of the investment portfolio data according to the second breakthrough decomposition formula and the share ratio data and the initial market value data of each ticket of the investment portfolio data at the initial time of the target time period.
Optionally, the preset multiple penetration decompositions include a third penetration decompositions, and the step of performing the penetration decomposition operation on the combined position taken data and the share ratio data according to the preset multiple penetration decompositions to obtain multiple intermediate data includes:
acquiring fund scale data, transaction data and unit net value data corresponding to each coupon of the investment portfolio data in a target time period;
and calculating to obtain trading position data corresponding to each ticket of the investment portfolio data according to the third breakthrough decomposition formula and the fund scale data, the trading data and the unit net value data corresponding to each ticket of the investment portfolio data in a target time period.
Optionally, the step of performing a breakthrough decomposition operation on the combined trading data and share ratio data through the preset multiple breakthrough decompositions to obtain market value data of each parent fund, trading data of each bottom layer data, and market value data of each bottom layer data in the combination includes:
acquiring position taking data of each mother fund in the investment portfolio data;
acquiring at least one sub fund of each mother fund;
monitoring a transaction change value of the at least one sub-fund for the corresponding underlying data, wherein the transaction change value is used for recording the transaction state of the underlying data;
and when the trading change value of the bottom layer data meets a preset trading condition, penetrating and analyzing the bottom layer data and the position data of the corresponding mother fund to obtain the market value data of the mother fund, the trading data of the bottom layer data and the market value data of the bottom layer data.
Optionally, the step of constructing a cash virtual transaction according to the market value data of each parent fund and the corresponding market value data of the underlying data includes:
calculating the difference value of the market value data of each mother fund and the market value data of the corresponding bottom layer data;
summing a plurality of said differences to obtain data for a cash virtual transaction.
Optionally, the portfolio data method further comprises: and generating the investment grade data of the target investor according to the penetration data:
analyzing each asset data in the investment portfolio data to obtain a benchmark type of each asset data;
determining a plurality of intermediate data with highest profit as target intermediate data from profit conclusion data corresponding to the plurality of intermediate data of each asset data;
analyzing the target intermediate data to obtain the asset type of the target intermediate data;
calculating the matching degree between the asset type of the target intermediate data and the reference type of the corresponding asset data;
generating investment grade data of target investors based on the matching degree: when the matching degree is greater than a preset threshold value, generating investment grade data of the target investor as a first grade; and when the matching degree is smaller than a preset threshold value, generating the investment grade data of the target investor as a second grade.
In order to achieve the above object, an embodiment of the present invention further provides a data processing system, including:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for extracting a plurality of analysis factors of investment portfolio data, classifying and packaging the analysis factors in a preset database, and the analysis factors comprise combined position data, combined transaction data and share ratio data;
the first penetration decomposition module is used for acquiring the combined position data and the share ratio data from the database, and performing penetration decomposition operation on the combined position data and the share ratio data according to a plurality of preset penetration decomposition formulas to obtain a plurality of intermediate data, wherein the plurality of intermediate data comprise end position data, initial position data and deal position data of a plurality of tickets in the investment combination data;
the virtual transaction construction module is used for constructing fund virtual transactions according to a preset fund virtual transaction template, the fund virtual transactions are used as first adjustment items, the fund virtual transactions are obtained by updating the fund virtual transaction template through the share ratio data, and the fund virtual transactions are used for simulating the purchase and redemption of the investment portfolio data;
the second penetration decomposition module is used for acquiring the combined transaction data and the share ratio data from the database, and performing penetration decomposition operation on the combined transaction data and the share ratio data through a plurality of preset penetration decomposition formulas to obtain a plurality of mother fund market value data, transaction data of each bottom layer data and market value data of each bottom layer data in the investment portfolio data, wherein each mother fund comprises at least one bottom layer data;
the cash establishing module is used for establishing cash virtual transaction according to the market value data of each mother fund and the corresponding market value data of the bottom layer data, the cash virtual transaction is used as a second adjustment item, and the cash virtual transaction is used for expressing the occupation ratio of cash in the corresponding sub-fund in the investment portfolio data; and
and the generating module is used for generating penetration data of the investment portfolio data according to the plurality of intermediate data, the first adjusting item and the second adjusting item and carrying out visualization processing, wherein the penetration data is used for representing income attribution of the investment portfolio data.
In order to achieve the above object, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the steps of the data processing method as described above when executing the computer program.
In order to achieve the above object, an embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, the computer program being executable by at least one processor to cause the at least one processor to execute the steps of the data processing method as described above.
The data processing method, the data processing system, the computer equipment and the computer readable storage medium provided by the embodiment of the invention analyze the investment portfolio data in multiple angles, such as a plurality of analysis factors, such as share ratio data, a plurality of penetration decomposition type, virtual transaction, cash flow data and the like, so that the accuracy of data processing is ensured, the calculated amount of the data is simplified, the data processing efficiency is improved, and the operation burden of a CPU is reduced.
The invention is described in detail below with reference to the drawings and specific examples, but the invention is not limited thereto.
Drawings
FIG. 1 is a flowchart illustrating steps of a data processing method according to a first embodiment of the present invention;
FIG. 2 is a flowchart illustrating steps of obtaining loan-related data in a data processing method according to an embodiment of the invention;
FIG. 3 is a flowchart illustrating a step of obtaining the end-of-term position taken data by penetration in the data processing method according to the first embodiment of the present invention;
FIG. 4 is a flowchart illustrating a step of obtaining initial taken position data through penetration in a data processing method according to a first embodiment of the present invention;
FIG. 5 is a flowchart illustrating a step of obtaining transaction taken position data through a data processing method according to a first embodiment of the present invention;
FIG. 6 is a flowchart illustrating the steps of the subgrain penetration in the data processing method according to the first embodiment of the present invention;
FIG. 7 is a flowchart illustrating steps of obtaining cash flow data in a data processing method according to an embodiment of the present invention;
FIG. 8 is a block diagram of a second embodiment of a data processing system;
fig. 9 is a schematic hardware structure diagram of a computer device according to a third embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the descriptions relating to "first", "second", etc. in the embodiments of the present invention are only for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
In the description of the present invention, it should be understood that the numerical references before the steps do not identify the order of performing the steps, but merely serve to facilitate the description of the present invention and to distinguish each step, and thus should not be construed as limiting the present invention.
Example one
Referring to fig. 1, a flowchart illustrating steps of a data processing method according to an embodiment of the invention is shown. It is to be understood that the flow charts in the embodiments of the present method are not intended to limit the order in which the steps are performed. The following description is given by taking a computer device as an execution subject, specifically as follows:
as shown in fig. 1, the data processing method may include steps S100 to S600, in which:
step S100, extracting a plurality of analysis factors of the investment portfolio data, classifying and packaging the analysis factors in a preset database, wherein the analysis factors comprise combined position data, combined transaction data and investment portfolio data quota data.
In an exemplary embodiment, the portfolio data may be portfolio data constructed by an investor. For example, portfolio data for a fund is typically configured from a broad class of assets, such as currencies, bonds, equities, QDII, commodities, and the like. Wherein, a certain investment portfolio data includes the variety category, industry, security type, style, scale, region, country, etc. of each asset. The share ratio data for each asset refers to the proportion of the share of the asset held by the user in the total share of the asset.
Step 200, acquiring the combined position data and the share ratio data from the database, and performing a penetrating decomposition operation on the combined position data and the share ratio data according to a plurality of preset penetrating decomposition formulas to obtain a plurality of intermediate data, wherein the plurality of intermediate data comprise end position data, initial position data and deal position data of a plurality of tickets in the investment portfolio data.
In an exemplary embodiment, the plurality of intermediate data includes end position data, initial position data and trading position data corresponding to each asset, which is generated by penetrating the combined position data and the share ratio data based on a plurality of preset penetration decompositions, and it is understood that a corresponding plurality of intermediate data of underlying data held by each asset, such as corresponding end position data, initial position data and trading position data of underlying data held by an a fund, such as stocks, cash, B fund and the like. The plurality of breakthrough decompositions includes a first breakthrough decomposition, a second breakthrough decomposition, and a third breakthrough decomposition.
Further, the first, second and third breakthrough decompositions are evolved based on the profit computation rule; the revenue calculation rule is a net revenue rule. Wherein the revenue calculation rule is as follows: (emv-bmv-rmv)/(bmv + prch), wherein, emv: end market value, bmv: initial market value, rmv: summary of cash flow for transactions, prch: the purchase amount.
Specifically, a first penetrating decomposition formula is obtained through conversion by combining a first association rule and a profit calculation rule, and a formula corresponding to the first association rule is as follows: unit _ net _ value ═ net _ value/lot, where unit _ net _ value (uv): net _ value (nv): net worth data, lot: number of portions.
And combining the second association rule and the profit calculation rule to obtain a second penetration decomposition formula through conversion, wherein the formula corresponding to the second association rule is as follows: qty × unv ═ ratio nv, where qty: number of positions, Ratio: ratio of the shares.
And combining a third association rule and a profit calculation rule to obtain a third penetration decomposition formula through conversion, wherein the formula corresponding to the third association rule is as follows: net _ value ═ sum (security + case), where sum is expressed as the sum, security: an individual ticket; and (2) cash: cash.
Further, the overall breakthrough decomposition formula is expressed as: (ratio (t) net _ value (t) -ratio (t-1) net _ value (t-1) -size unit _ net _ value (t-1))/(ratio (t-1) net _ value (t-1) + size (buy) unit _ net _ value (t-1)).
In an exemplary embodiment, referring to fig. 2, the predetermined plurality of breakthrough decompositions includes a first breakthrough decomposition, and the step S200 may further include steps S101 to S102, wherein: step S201, acquiring share ratio data and end market value data of each coupon of the investment portfolio data in the final time of a target time period; step S202, analyzing share ratio data of each ticket of the investment portfolio data in the final time of the target time period and the end market value data according to the first penetrating decomposition formula to obtain end position data corresponding to each ticket of the investment portfolio data.
Illustratively, the first pass-through decomposition formula is evolved to: the end-of-term position-taken profit data is share ratio data, and the end-of-term market value of the instrument is taken. The target time period is (t-1) to t, and the final time corresponds to t, t >0, that is, the end-of-term position holding data (ratio) (t) net _ value (t).
In an exemplary embodiment, referring to fig. 3, the predetermined plurality of breakthrough decompositions includes a second breakthrough decomposition, and the step S200 may further include: step S211, acquiring share ratio data and initial market value data of each coupon of the investment portfolio data at the initial time of a target time period; step S212, according to the second breakthrough decomposition formula, analyzing share ratio data and initial market value data of each ticket of the portfolio data at an initial time of a target time period to obtain initial position data corresponding to each ticket of the portfolio data.
Specifically, the second breakthrough decomposition formula is evolved to obtain: and the initial position taking yield data is share ratio data, and the initial market value of the instrument position taking is obtained. The initial time corresponds to t, t >0, i.e. the initial bin data ratio (t-1) net value (t-1).
In an exemplary embodiment, as shown in fig. 4, the preset plurality of breakthrough decompositions includes a third breakthrough decomposition formula, and the step S200 may further include: step S221, acquiring fund scale data, transaction data and unit net value data corresponding to each coupon of the investment portfolio data in a target time period; step S222, analyzing fund scale data, transaction data and unit net worth data corresponding to each coupon of the investment portfolio data in a target time period according to the third breakthrough decomposition formula, so as to obtain transaction position data corresponding to each coupon of the investment portfolio data.
According to the formula corresponding to the penetration decomposition formula, the penetration of the position holding of the asset data is the result of adjusting the fund according to the share ratio. And calculating the fraction ratio of the last position taken after the penetration to obtain the last position taken after the penetration, wherein the last position taken at the early stage after the penetration can be directly obtained.
Step S300, constructing a fund virtual transaction according to a preset fund virtual transaction template, wherein the fund virtual transaction is used as a first adjustment item, is obtained by updating the fund virtual transaction template through the share ratio data, and is used for simulating the purchase and redemption of the investment portfolio data.
Due to the adjustment caused by the change of share ratio, the method is complex, combines the purchase and redemption of fund, and can not clearly obtain the result according to the purchase and redemption of each fund. Therefore, it is necessary to simplify the influence of the share ratio, and a virtual transaction is constructed, i.e. dilution/back-dilution by subscription and redemption, is reflected in the final position of the hold by the corresponding transaction.
And constructing virtual transaction data based on the share ratio data through a formula sum (b _ ratio (t-1) × pv (t-1)) - [ sum (buy) ], and sum (s _ ratio (t-1) × pv (t-1)) - [ sum (fe + sel) ], so as to obtain the virtual transaction data generated corresponding to each asset. Wherein, buy and b represent purchase requisition, sel and s represent redemption, and fe represents handling fee; pv represents the net value of the subscription or redemption.
Assuming that the A fund has 100 shares, the first holds 50 shares of the A fund, and the first sells 25 shares of the A fund today, i.e. the share ratio of the first holding changes from 50% to 25%, because the share ratio of trading behavior changes. Assuming that the A fund is not sold by the first today and still holds 50A funds, but the A fund is purchased 100A funds the next day, so that the total share of the A fund changes, and the A fund has 200 shares, at this time, even if the actual trading action does not occur on the first, the share ratio of the A fund held by the first changes, which is equivalent to diluting by 25%, the profit can be calculated through the constructed virtual trading, and the diluted 25% is equivalent to selling the 5% part at the price of T-1. And analyzing the transaction data to obtain final share ratio data.
Step S400, acquiring the combined transaction data and the share ratio data from the database, and performing a penetrating decomposition operation on the combined transaction data and the share ratio data through the preset plurality of penetrating decomposition formulas to obtain a plurality of mother fund market value data, transaction data of each bottom layer data and market value data of each bottom layer data in the investment combination data, wherein each mother fund comprises at least one bottom layer data.
For example, assume that A fund is a mother fund, A fund holds sub-funds such as B fund, C fund, D fund and the like, and shares, cash and E fund held by B fund are underlying data
In an exemplary embodiment, referring to fig. 5, the step S500 may further include: step S501, acquiring position data of each mother fund in the investment portfolio data; step S502, at least one sub fund of each mother fund is obtained; step S503, monitoring the transaction change value of the at least one sub fund to the corresponding bottom layer data, wherein the transaction change value is used for recording the transaction state of the bottom layer data; step S504, when the trading change value of the bottom layer data meets the preset trading condition, the bottom layer data and the position holding data of the corresponding mother fund are penetratingly analyzed to obtain the market value data of the mother fund, the trading data of the bottom layer data and the market value data of the bottom layer data.
The preset trading condition is that when the trading change value of the bottom layer data meets the penetration ratio of the bottom layer data and the corresponding sub-fund, the market value data of the mother fund, the trading data of the bottom layer data and the market value data of the bottom layer data are obtained.
Step S500, according to the market value data of each mother fund and the market value data of the corresponding bottom layer data, a cash virtual transaction is constructed and used as a second adjustment item, and the cash virtual transaction is used for expressing the occupation ratio of cash in the corresponding sub-fund in the investment portfolio data.
In an exemplary embodiment, as shown in fig. 6, the step S600 may be further obtained by: step S601, calculating the difference value of the market value data of each mother fund and the market value data of the corresponding bottom layer data; step S602, summing a plurality of the differences to obtain cash flow data.
Illustratively, the total value of the penetrated fund market value is subtracted by the market value of the penetrated ticket to directly obtain a final summarized cash difference part, and a plurality of cash difference parts are summarized to obtain cash flow data.
The collected cash flow data can be understood as collected cash items, cash of all held sub-funds in the investment portfolio data is collected as a unique subject, and the total cash is used as the cash items for adjustment; the cash flow data is handled well.
Step S600, generating penetration data of the investment portfolio data according to the plurality of intermediate data, the first adjustment items and the second adjustment items, and performing visualization processing, wherein the penetration data is used for representing income attribution investment portfolio data of the investment portfolio data.
In an exemplary embodiment, the penetration data includes return outcome data, wherein the return outcome data refers to a return on investment achieved by the initial investment in the investment gains (including capital gains, dividend revenues, interest revenues, etc.) of the portfolio data over a period of time.
In an exemplary embodiment, a revenue form is generated based on the revenue conclusion data.
In an exemplary embodiment, referring to fig. 7, the method further comprises: analyzing the income conclusion data to obtain investor grade data corresponding to the investment combination data, and feeding the investor grade data back to the client; the method comprises the following specific steps: step S801, analyzing each asset data in the investment portfolio data to obtain a reference type of each asset data; step S802, determining a plurality of intermediate data with highest profit as target intermediate data from profit conclusion data corresponding to a plurality of intermediate data of each asset data; step S803, analyzing the target intermediate data to obtain the asset type of the target intermediate data; step S804, calculating the matching degree between the asset type of the target intermediate data and the reference type of the corresponding asset data; step S805, generating investment grade data of target investors based on the matching degree: when the matching degree is greater than a preset threshold value, generating investment grade data of the target investor as a first grade; and when the matching degree is smaller than a preset threshold value, generating the investment grade data of the target investor as a second grade.
Specifically, the first level has a higher investment capacity for the target investor, and the second level has a lower investment capacity for the target investor.
Illustratively, the investment level data obtained from the analysis of the revenue conclusion data is filled into a revenue form.
The embodiment of the invention analyzes the investment portfolio data through a plurality of angles such as share ratio data, evolved penetration rules, virtual transactions, cash flow data and the like to obtain income attribution data, and then analyzes the investment grade data of investors according to the income attribution data to improve the accuracy of investment data analysis.
The application can penetrate the sub-fund, the obtained coupon income data is matched with the current coupon market, and the accuracy of the data is guaranteed. The constructed virtual transaction is used as an adjustment item of the income of each coupon, and the summarized cash virtual transaction is used as an integral adjustment item, so that the accuracy is ensured. Converting the impact of the procurement and redemption to a ratio of shares at the end of each day simplifies the calculation of the revenue conclusion data.
The invention analyzes the investment portfolio data by a plurality of analysis factors such as share ratio data, a plurality of penetration decomposition type, virtual transaction, cash flow data and other multi-angle analysis investment portfolio data, ensures the accuracy of data processing, simplifies the calculated amount of the data, improves the processing efficiency of the data, and reduces the operation burden of a CPU
The embodiment of the invention also has the following technical effects:
(1) the data processing process and logic are simplified, the data processing capacity is improved, and the problem which is not 100% held can be processed by simple and rigorous rules.
(2) The method and the device ensure the rigor of a profit system, obtain profit results without residual errors, and improve the accuracy of data processing.
(3) Due to the rigor and rule simplicity of the system, the benefit system in the embodiment of the invention can quickly analyze the benefits through the obtained penetration data, thereby greatly reducing the calculated amount of the data.
(4) The income system of the embodiment of the invention obtains subsequent income attribution through an income rate algorithm and supports detailed analysis of the source of the income. In the non-penetration case, only revenue from one fund can be analyzed, while in the penetration case, revenue can be analyzed to a finer granularity, to the granularity of a single instrument.
(5) The income system of the embodiment of the invention has high data processing efficiency, simplifies various steps such as intermediate transaction, share change and the like, constructs virtual transaction data directly through results, accelerates the processing efficiency of the income system for the penetration of investment portfolio data, duplicates the calculation process of income, and ensures the accuracy of data processing.
Example two
With continued reference to FIG. 8, a program module diagram of a data processing system in accordance with the present invention is shown. In the present embodiment, the data processing system 20 may include or be divided into one or more program modules, and the one or more program modules are stored in a storage medium and executed by one or more processors to implement the present invention and implement the above-described data processing method. Program modules in accordance with embodiments of the present invention may be referred to as a series of computer program instruction segments that perform particular functions, and may be more suitable than programs themselves for describing the execution of data processing system 20 on a storage medium. The following description will specifically describe the functions of the program modules of the present embodiment:
a first obtaining module 900, configured to extract a plurality of analysis factors of the investment portfolio data, and classify and encapsulate the plurality of analysis factors in a preset database, where the plurality of analysis factors include combined position data, combined transaction data, and share ratio data;
a first breakthrough decomposition module 910, configured to obtain the combined position data and share ratio data from the database, and perform a breakthrough decomposition operation on the combined position data and share ratio data according to a plurality of preset breakthrough decomposition formulas to obtain a plurality of intermediate data, where the plurality of intermediate data includes end position data, initial position data, and deal position data of a plurality of tickets in the investment portfolio data;
a virtual transaction construction module 920, configured to construct a virtual transaction of a fund according to a preset virtual transaction template of the fund, where the virtual transaction of the fund is obtained by updating the virtual transaction template of the fund according to the share ratio data, and is used to simulate the purchase and redemption of the investment portfolio data;
a second breakthrough decomposition module 930, configured to perform breakthrough decomposition on the combined transaction data and share ratio data through the preset multiple breakthrough decompositions to obtain market value data of a parent fund in the combination, transaction data of each bottom layer data, and market value data of each bottom layer data, where each parent fund includes at least one bottom layer data;
a cash constructing module 940, configured to construct a cash virtual transaction according to the market value data of each parent fund and the corresponding market value data of the underlying data, where the cash virtual transaction is used as a second adjustment item, and the cash virtual transaction is used to represent a proportion of cash in a corresponding sub-fund in the investment portfolio data; and
a generating module 950, configured to generate penetration data of the portfolio data according to the plurality of intermediate data, the first adjustment item, and the second adjustment item, and perform visualization processing, where the penetration data is used to characterize income attribution of the portfolio data. .
EXAMPLE III
Fig. 9 is a schematic diagram of a hardware architecture of a computer device according to a third embodiment of the present invention. In the present embodiment, the computer device 2 is a device capable of automatically performing numerical calculation and/or information processing in accordance with a preset or stored instruction. The computer device 2 may be a rack server, a blade server, a tower server or a rack server (including an independent server or a server cluster composed of a plurality of servers), and the like. As shown in FIG. 9, the computer device 2 includes, but is not limited to, at least a memory 21, a processor 22, a network interface 23, and a data processing system 20, which may be communicatively coupled to each other via a system bus. Wherein:
in this embodiment, the memory 21 includes at least one type of computer-readable storage medium including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 21 may be an internal storage unit of the computer device 2, such as a hard disk or a memory of the computer device 2. In other embodiments, the memory 21 may also be an external storage device of the computer device 2, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like provided on the computer device 2. Of course, the memory 21 may also comprise both internal and external memory units of the computer device 2. In this embodiment, the memory 21 is generally used for storing an operating system installed in the computer device 2 and various types of application software, such as the program codes of the data processing system 20 of the above-mentioned embodiment. Further, the memory 21 may also be used to temporarily store various types of data that have been output or are to be output.
The network interface 23 may comprise a wireless network interface or a wired network interface, and the network interface 23 is generally used for establishing communication connection between the computer device 2 and other electronic apparatuses. For example, the network interface 23 is used to connect the computer device 2 to an external terminal through a network, establish a data transmission channel and a communication connection between the computer device 2 and the external terminal, and the like. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), Wi-Fi, and the like.
It is noted that fig. 9 only shows the computer device 2 with components 20-23, but it is to be understood that not all shown components are required to be implemented, and that more or less components may be implemented instead.
In this embodiment, the data processing system 20 stored in the memory 21 can be further divided into one or more program modules, and the one or more program modules are stored in the memory 21 and executed by one or more processors (in this embodiment, the processor 22) to complete the present invention.
For example, fig. 8 shows a schematic diagram of program modules implementing a second embodiment of the data processing system 20, in which embodiment the data processing system 20 may be divided into a first obtaining module 900, a first breakthrough resolving module 910, a virtual transaction building module 920, a second breakthrough resolving module 930, a build cash module 940, and a generating module 950. Herein, the program modules referred to herein are a series of computer program instruction segments that can perform specific functions, and are more suitable than programs for describing the execution process of the data processing system 20 in the computer device 2. The specific functions of the program module 900 and 950 have been described in detail in the second embodiment, and are not described herein again.
Example four
The present embodiment also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc., on which a computer program is stored, which when executed by a processor implements corresponding functions. The computer readable storage medium of the present embodiment is used for storing the data processing system 20, and when being executed by a processor, the computer readable storage medium implements the data processing method of the above embodiment.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A data processing method, comprising:
extracting a plurality of analysis factors of the investment portfolio data, classifying and packaging the analysis factors in a preset database, wherein the analysis factors comprise combined position data, combined transaction data and investment portfolio data share ratio data;
acquiring the combined position data and share ratio data from the database, and performing a penetrating decomposition operation on the combined position data and the share ratio data according to a plurality of preset penetrating decomposition formulas to obtain a plurality of intermediate data, wherein the plurality of intermediate data comprise end position data, initial position data and transaction position data of a plurality of tickets in the investment combination data;
constructing a fund virtual transaction according to a preset fund virtual transaction template, wherein the fund virtual transaction is obtained by updating the fund virtual transaction template through the share ratio data and is used for simulating the subscription and redemption of the investment portfolio data;
acquiring the combined transaction data and share ratio data from the database, and performing a penetrating decomposition operation on the combined transaction data and share ratio data through a plurality of preset penetrating decomposition formulas to obtain a plurality of mother fund market value data, transaction data of each bottom layer data and market value data of each bottom layer data in investment combination data, wherein each mother fund comprises at least one bottom layer data;
according to the market value data of each mother fund and the corresponding market value data of the bottom layer data, cash virtual transactions are constructed and used as second adjustment items, and the cash virtual transactions are used for expressing the occupation ratio of cash in corresponding sub-funds in the investment portfolio data; and
and generating penetration data of the investment portfolio data according to the plurality of intermediate data, the first adjustment items and the second adjustment items, and performing visualization processing, wherein the penetration data is used for representing income attribution investment portfolio data of the investment portfolio data.
2. The data processing method according to claim 1, wherein the predetermined plurality of breakthrough decompositions comprises a first breakthrough decompositions, and the step of performing a breakthrough decomposition operation on the combined position data and the share ratio data according to the predetermined plurality of breakthrough decompositions to obtain a plurality of intermediate data comprises:
acquiring share ratio data and end market value data of each coupon of the portfolio data at the final time of a target time period;
and calculating to obtain the end position data corresponding to each ticket of the investment portfolio data according to the first breakthrough decomposition formula and the share ratio data and the end market value data of each ticket of the investment portfolio data in the final time of the target time period.
3. The data processing method according to claim 2, wherein the predetermined plurality of breakthrough decompositions comprises a second breakthrough decompositions, and the step of performing a breakthrough decomposition operation on the combined position data and the share ratio data according to the predetermined plurality of breakthrough decompositions to obtain a plurality of intermediate data comprises:
acquiring share ratio data and initial market value data of each coupon of the investment portfolio data at the initial time of a target time period;
and calculating to obtain initial position data corresponding to each ticket of the investment portfolio data according to the second breakthrough decomposition formula and the share ratio data and the initial market value data of each ticket of the investment portfolio data at the initial time of the target time period.
4. The data processing method according to claim 3, wherein the predetermined plurality of breakthrough decompositions comprises a third breakthrough decompositions, and the step of performing a breakthrough decomposition operation on the combined position data and the share ratio data according to the predetermined plurality of breakthrough decompositions to obtain a plurality of intermediate data comprises:
acquiring fund scale data, transaction data and unit net value data corresponding to each coupon of the investment portfolio data in a target time period;
and calculating to obtain trading position data corresponding to each ticket of the investment portfolio data according to the third breakthrough decomposition formula and the fund scale data, the trading data and the unit net value data corresponding to each ticket of the investment portfolio data in a target time period.
5. The data processing method according to claim 3, wherein the step of performing a breakthrough decomposition operation on the combined trading data and share ratio data through the preset plurality of breakthrough decompositions to obtain trading data of each mother fund market value data, each underlying data, and market value data of each underlying data in the combination comprises:
acquiring position taking data of each mother fund in the investment portfolio data;
acquiring at least one sub fund of each mother fund;
monitoring a transaction change value of the at least one sub-fund for the corresponding underlying data, wherein the transaction change value is used for recording the transaction state of the underlying data;
and when the trading change value of the bottom layer data meets a preset trading condition, penetrating and analyzing the bottom layer data and the position data of the corresponding mother fund to obtain the market value data of the mother fund, the trading data of the bottom layer data and the market value data of the bottom layer data.
6. The data processing method of claim 5, wherein the step of constructing a cash virtual transaction based on the value data of each parent fund and the corresponding underlying data comprises:
calculating the difference value of the market value data of each mother fund and the market value data of the corresponding bottom layer data;
summing a plurality of said differences to obtain data for a cash virtual transaction.
7. The data processing method of claim 1, wherein the portfolio data method further comprises: and generating the investment grade data of the target investor according to the penetration data:
analyzing each asset data in the investment portfolio data to obtain a benchmark type of each asset data;
determining a plurality of intermediate data with highest profit as target intermediate data from profit conclusion data corresponding to the plurality of intermediate data of each asset data;
analyzing the target intermediate data to obtain the asset type of the target intermediate data;
calculating the matching degree between the asset type of the target intermediate data and the reference type of the corresponding asset data;
generating investment grade data of target investors based on the matching degree: when the matching degree is greater than a preset threshold value, generating investment grade data of the target investor as a first grade; and when the matching degree is smaller than a preset threshold value, generating the investment grade data of the target investor as a second grade.
8. A data processing system, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for extracting a plurality of analysis factors of investment portfolio data, classifying and packaging the analysis factors in a preset database, and the analysis factors comprise combined position data, combined transaction data and share ratio data;
the first penetration decomposition module is used for acquiring the combined position data and the share ratio data from the database, and performing penetration decomposition operation on the combined position data and the share ratio data according to a plurality of preset penetration decomposition formulas to obtain a plurality of intermediate data, wherein the plurality of intermediate data comprise end position data, initial position data and deal position data of a plurality of tickets in the investment combination data;
the virtual transaction construction module is used for constructing fund virtual transactions according to a preset fund virtual transaction template, the fund virtual transactions are used as first adjustment items, the fund virtual transactions are obtained by updating the fund virtual transaction template through the share ratio data, and the fund virtual transactions are used for simulating the purchase and redemption of the investment portfolio data;
the second penetration decomposition module is used for acquiring the combined transaction data and the share ratio data from the database, and performing penetration decomposition operation on the combined transaction data and the share ratio data through a plurality of preset penetration decomposition formulas to obtain a plurality of mother fund market value data, transaction data of each bottom layer data and market value data of each bottom layer data in the investment portfolio data, wherein each mother fund comprises at least one bottom layer data;
the cash establishing module is used for establishing cash virtual transaction according to the market value data of each mother fund and the corresponding market value data of the bottom layer data, the cash virtual transaction is used as a second adjustment item, and the cash virtual transaction is used for expressing the occupation ratio of cash in the corresponding sub-fund in the investment portfolio data; and
and the generation module is used for generating penetration data of the investment portfolio data according to the plurality of intermediate data, the first adjustment items and the second adjustment items and carrying out visualization processing, wherein the penetration data is used for representing income attribution investment portfolio data of the investment portfolio data.
9. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the data processing method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which is executable by at least one processor for causing the at least one processor to carry out the steps of the data processing method according to any one of claims 1 to 7.
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